What the Success of Brain Imaging Implies about the Neural Code

The success of fMRI places constraints on the nature of the neural code. The fact that researchers can infer similarities between neural representations, despite fMRI's limitations, implies that certain neural coding schemes are more likely than others. For fMRI to succeed given its low temporal and spatial resolution, the neural code must be smooth at the voxel and functional level such that similar stimuli engender similar internal representations. Through proof and simulation, we determine which coding schemes are plausible given both fMRI's successes and its limitations in measuring neural activity. Deep neural network approaches, which have been forwarded as computational accounts of the ventral stream, are consistent with the success of fMRI, though functional smoothness breaks down in the later network layers. These results have implications for the nature of neural code and ventral stream, as well as what can be successfully investigated with fMRI.

功能性磁共振成像（fMRI）的成功对神经编码的本质提出了限制。尽管fMRI在时间和空间分辨率上的局限性，研究人员仍能推断出神经表征之间的相似性，这一事实暗示了某些神经编码方案相较于其他方案更有可能。为了fMRI能够在低时间分辨率和空间分辨率的情况下取得成功，神经编码必须在体素和功能层面上保持平滑，如此一来，相似的刺激才能产生相似的内部表征。通过实证研究和模拟实验，我们确定了在fMRI的成功及其在测量神经活动上的局限性背景下，哪些编码方案是可行的。深度神经网络方法，作为腹侧通路计算解释的候选，与fMRI的成功相一致，尽管在网络的后期层中，功能平滑性出现了破裂。这些研究结果对神经编码的本质、腹侧通路以及fMRI能够成功探究的内容产生了重要影响。